Data Collection Issues Research Articles

ISB recommendations for skin-marker-based multi-segment foot kinematics

The foot is anatomically and functionally complex, and thus an accurate description of intrinsic kinematics for clinical or sports applications requires multiple segments. This has led to the development of many multi-segment foot models for both kinematic and kinetic analyses. These models differ in the number of segments analyzed, bony landmarks identified, required marker set, defined anatomical axes and frames, the convention used to calculate joint rotations and the determination of neutral positions or other offsets from neutral. Many of these models lack validation. The terminology used is inconsistent and frequently confusing. Biomechanical and clinical studies using these models should use established references and describe how results are obtained and reported.The International Society of Biomechanics has previously published proposals for standards regarding kinematic and kinetic measurements in biomechanical research, and in this paper also addresses multi-segment foot kinematics modeling. The scope of this work is not to prescribe a particular set of standard definitions to be used in all applications, but rather to recommend a set of standards for collecting, calculating and reporting relevant data.The present paper includes recommendations for the overall modeling and grouping of the foot bones, for defining landmarks and other anatomical references, for addressing the many experimental issues in motion data collection, for analysing and reporting relevant results and finally for designing clinical and biomechanical studies in large populations by selecting the most suitable protocol for the specific application.These recommendations should also be applied when writing manuscripts and abstracts.

Thinking about police data: Analysts’ perceptions of data quality in Canadian policing

Policing is increasingly being shaped by data collection and analysis. However, we still know little about the quality of the data police services acquire and utilize. Drawing on a survey of analysts from across Canada, this article examines several data collection, analysis, and quality issues. We argue that as we move towards an era of big data policing it is imperative that police services pay more attention to the quality of the data they collect. We conclude by discussing the implications of ignoring data quality issues and the need to develop a more robust research culture in policing.

Analyzing and improving tools for supporting fighting against COVID-19 based on prediction models and contact tracing

In the paper, we are analyzing and proposing an improvement to current tools and solutions for supporting fighting with COVID-19. We analyzed the most popular anti-covid tools and COVID prediction models. We addressed issues of secure data collection, prediction accuracy based on COVID models. What is most important, we proposed a solution for improving the prediction and contract tracing element in these applications. The proof of concept solution to support the fight against a global pandemic is presented, and the future possibilities for its development are discussed. © 2021 The Author(s).

Model of Data Collection Control in the Internet of Things on the Basis of Social Network Technologies

The paper deals with the issues of data collection in the dynamic Internet of Things network. An approach proposed is based on the models and methods of social networks analysis along with clustering methods commonly used in this case. The developed hybrid model tracks contacts between nodes of the network thus creating “social relationships” for each node and estimates the number and the frequency of “social contacts” characterizing the degree of “friendships”. After fixing the contacts between nodes the model takes into consideration only the nodes connected by “friendship” relation. Data collection based on “social structure” provides higher security level and shows the enhancement as regards energy saving and delays of data transfer.

Changing migration rates in England and Wales over a 40‐year period: Is mobility declining?

AbstractResearch using data for the United States has pointed towards a long‐term pattern of falling migration rates. Similar research in England and Wales has also found declining migration rates when observing all migrants. Using the Office for National Statistics Longitudinal Study (ONS LS), we study the patterns of migration observed over 10‐year periods for persons in 10‐year birth cohorts over each intercensal period from 1971–1981 through to 2001–2011.When the sample population is considered as a whole, the results from the LS are consistent with earlier findings: There has been a decade‐on‐decade decline in the migration rate as observed in longitudinal data. When disaggregated by cohort, it is apparent that this decline is not always observed for young adults, but it is consistently observed for those in their 30s and older, for whom each successive cohort has a lower migration rate than the previous one and also for whom there is a steady within‐cohort decline in the migration rate over time. This is observed at both short and longer distances of move. Alternative results from successive censuses looking at migration observed for single years do not appear to confirm this decline in migration, although data collection and processing issues may be part of the explanation for this finding, and it is also the case that these data may be more subject to cyclical effects.

Improving mobility and participation of older people with vertigo, dizziness and balance disorders in primary care using a care pathway: feasibility study and process evaluation

BackgroundCommunity-dwelling older people are frequently affected by vertigo, dizziness and balance disorders (VDB). We previously developed a care pathway (CPW) to improve their mobility and participation by offering standardized approaches for general practitioners (GPs) and physical therapists (PTs). We aimed to assess the feasibility of the intervention, its implementation strategy and the study procedures in preparation for the subsequent main trial.MethodsThis 12-week prospective cohort feasibility study was accompanied by a process evaluation designed according to the UK Medical Research Council’s Guidance for developing and evaluating complex interventions. Patients with VDB (≥65 years), GPs and PTs in primary care were included. The intervention consisted of a diagnostic screening checklist for GPs and a guide for PTs. The implementation strategy included specific educational trainings and a telephone helpline. Data for mixed-method process evaluation were collected via standardized questionnaires, field notes and qualitative interviews. Quantitative data were analysed using descriptive statistics, qualitative data using content analysis.ResultsA total of five GP practices (seven single GPs), 10 PT practices and 22 patients were included in the study. The recruitment of GPs and patients was challenging (response rates: GP practices: 28%, PT practices: 39%). Ninety-one percent of the patients and all health professionals completed the study. The health professionals responded well to the educational trainings; the utilization of the telephone helpline was low (one call each from GPs and PTs). Familiarisation with the routine of application of the intervention and positive attitudes were emphasized as facilitators of the implementation of the intervention, whereas a lack of time was mentioned as a barrier. Despite difficulties in the GPs’ adherence to the intervention protocol, the GPs, PTs and patients saw benefit in the intervention. The patients’ treatment adherence to physical therapy was good. There were minor issues in data collection, but no unintended consequences.ConclusionAlthough the process evaluation provided good support for the feasibility of study procedures, the intervention and its implementation strategy, we identified a need for improvement in recruitment of participants, the GP intervention part and the data collection procedures. The findings will inform the main trial to test the interventions effectiveness in a cluster RCT.Trial registrationProjektdatenbank Versorgungsforschung Deutschland (German registry Health Services Research) VfD_MobilE-PHY_17_003910, date of registration: 30.11.2017; Deutsches Register Klinischer Studien (German Clinical Trials Register) DRKS00022918, date of registration: 03.09.2020 (retrospectively registered).

Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN–CNN Approach

This letter studies deep learning-based efficient ground vibration monitoring systems. In this work, artificial intelligence (AI) techniques are adopted to effectively deal with practical issues of data collection and classification. Specifically, we develop a novel energy-efficient data collection scheme by adopting deep Q-network-based reinforcement learning. Also, we propose an enhanced joint recurrent neural network (RNN) and convolutional neural network (CNN) approach for ground vibration classification. The performance of the proposed scheme is evaluated using real-world ground vibration data. The experimental results show that the proposed classification scheme outperforms the best existing scheme with CNN by more than 13% in terms of classification accuracy. It is also shown that the proposed energy management scheme can improve the accuracy of the proposed ground vibration monitoring system by 7.6% over the comparable scheme using equal power allocation.

Guidance for design and analysis of observational studies of fetal and newborn outcomes following COVID-19 vaccination during pregnancy

COVID-19 vaccines are now being deployed as essential tools in the public health response to the global SARS-CoV-2 pandemic. Pregnant individuals are a unique subgroup of the population with distinctive considerations regarding risk and benefit that extend beyond themselves to their fetus/newborn. As a complement to traditional pharmacovigilance and clinical studies, evidence to comprehensively assess COVID-19 vaccine safety in pregnancy will need to be generated through observational epidemiologic studies in large populations. However, there are several unique methodological challenges that face observational assessments of vaccination during pregnancy, some of which may be more pronounced for COVID-19 studies. In this contribution, we discuss the most critical study design, data collection, and analytical issues likely to arise. We offer brief guidance to optimize the quality of such studies to ensure their maximum value for informing public health decision-making.

Vaccine Clinical Trials and Data Infrastructure

We find ourselves at a momentous turn in the history of vaccines. The COVID-19 pandemic triggered a quasi-global vaccine race that not only compressed vaccine research and development (R&D) timelines, but also paved the way for the administration of a new type of vaccine technology – mRNA vaccines, which work in substantially different ways from the vaccines in use before the pandemic. While the process of bringing emerging COVID-19 vaccines to market has taken place in an unusually short timeframe, it was largely predicated on the same scientific and regulatory processes that govern the development, approval and deployment of new vaccines. For decades, these processes have encompassed several phases of vaccine testing – first without and subsequently with the involvement of human subjects – followed by an analysis of the emerging data. This Essay reflects on the evolution and status quo of the ways in which these data are gathered and disseminated within the context of the development of new vaccines. It treats information stemming from clinical trials as the initial building blocks of our vaccine data infrastructure, and surveys problems related to data collection and disclosure that have long been pervasive in the vaccine R&D ecosystem.Part I of the Essay situates the discussion of vaccine clinical trial data within historical boundaries. Part I.A travels back in time to the polio vaccine trials of the 1950s in the United States, which were one of the main catalysts of the adoption of the clinical trial structure now in place throughout the world. Part I.B then charts the formalization of the modern vaccine clinical trial model through legislation adopted between the polio and the COVID-19 vaccine races.Even though this formalization has resulted in a seemingly robust legal framework, there remain multiple problems that affect both the ways in which vaccine clinical trial data is actually generated and then utilized. Using examples from both past vaccine clinical trials and the COVID-19 vaccine race, Part II.A focuses on data collection issues, with an emphasis on the under-representation of minority populations in vaccine clinical trials. Part II.B then considers how imperfectly generated data meet further roadblocks in the form of delayed reporting or lack of reporting of clinical trial results, as well as restrictions to data sharing often attributable to agency interpretations of trade secrecy provisions that have long been disputed by several legal scholars. These problems affect both the transparency and accountability of vaccine innovation processes, and pose significant hurdles to follow-on R&D. Moreover, and relatedly, they can impair public trust on vaccine innovation processes at a time in which vaccine misinformation is quickly eroding overall levels of trust in vaccination as a public health tool. Part III concludes the Essay by pointing towards emerging ways to enrich the existing vaccine clinical trial data infrastructure. Specifically, it provides a short case study on the COVID-19 data sharing policy implemented in the European Union by its counterpart to the U.S. Food and Drug Administration, the European Medicines Agency. This ad hoc policy quickly expanded the disclosure of information about emerging COVID-19 drugs and vaccines in response to mounting pressure for more transparency about the drug and vaccine approval process. As such, it may be used as a blueprint by regulators elsewhere, as well as by proponents of a more robust system for the disclosure and sharing of clinical trial data.

The Stakeholder Agreement Metric: Quantifying Preference Agreement Between Product Stakeholders

Abstract Go/no-go decisions require engineering design teams to evaluate whether a concept is worth further investment of resources. These decisions can be difficult when product success depends on multiple stakeholders in addition to the end-user. This study proposes the Stakeholder Agreement Metric (SAM) framework to estimate the level of agreement between stakeholder preferences via the distance between optimal designs calculated from a preference model derived from conjoint analysis. The framework was tested in an empirical case study describing the design and piloting of a hand tool for informal electronic waste workers in Thailand. Data from a follow-up assessment indicate the SAM estimate aligned with future metrics of stakeholder satisfaction. The case study also qualitatively compared SAM to the analytical hierarchy process (AHP). Data collection issues with AHP illustrated some of the practical limitations of the framework. This study suggests that the SAM framework is a promising tool to further explore as a way to support designers making go/no-go decisions that involve multiple stakeholders. Further exploration should include additional case studies to investigate potential outcomes of different SAM values and comparing multiple stakeholder groups.

The use of modern technologies in statistical data collection

This article describes statistical data, their types, methods of obtaining information, methods of statistical analysis of them, as well as questionnaires from the types of conducting statistical research. The issue of data collection and statistical analysis through online questionnaire was described in detail on the basis of vital issue.

Convergence of IOT in Tourism Industry: A Pragmatic Analysis

The term IoT commonly used abbreviation of ‘Internet of Things’ is trending and ascertaining the possible disruption in the tourism and hospitality industry. The industry is a complex interaction of several stakeholders and ancillaries working together for the experience of the tourist. It includes host government, local population, attractions, transportation, accommodation, food, and Beverages etc. tightly embodying the local trade, art, craft, festivals, religion, culture and many more. The tourist is the end consumer seeking experience and justifying with his Imagescape. The Imagescape could be different for the same destination in different people’s mind. The destination management companies work in order to continue, refine, or modify the current destination image to increase the tourist flow. With the IoT, Big Data, blockchain technologies, the destination will become a closely monitored space for the behavior of tourists, shopping patterns, visitation to each attraction, time spend and monetarily benefits generated. The reports will lead to well-calculated decision making for destination management and development of tourism and ultimately impacting the GDP growth. The Tourism industry has always been witnessing the changes and has been upgrading its attire accordingly. There was a time when internet overwhelmed the industry with multiple upgradations and now the Internet of things is going to influence the industry and tourist vividly.Tourism has been changing with the shifting technologies for several years and has shown multiple advancements to its operations and processes. Another technological trend of IoT is converting Tourism to the Smart Tourism and IoT is the core technology to transform the tourism industry with the help of cloud computing, mobile communication, blockchain, big data and artificial intelligence aligned to enhance the tourist experience. The hospitality industry is counted mainly on the utmost service standards and their executions with intangible characteristics. In such a complex industry, the innovative technology IoT has begun its influence to seek the customer’s satisfaction, cost saving, and business profit. Apart from the personalized rooms, predictive repair and maintenance the IoT will be enhancing the guest experience as well through the Electronic Key Cards which is sent by the hotel on your mobile phone to access the room directly without wasting their precious time in the reception of hotel. The hotel may also charge the guest as per the removal of beers cans from the mini bar of the room without the additional run through of a staff.The paper is crafted to identify the impacts of IoT on the tourism industry and proposes the model to streamline the industry with tourist’s movement in order to analyze the tourist’s experience completely. The Tourism industry is widespread and includes several stakeholders in overall channel of the tourist circuit. The industry is full of diversities and experiences in diversities. The trending technology may act as a thread to sew all in one comprising the experience and solve the issues of accurate data collection in the tourism industry. Fourth revolution technologies all together making an impact on the working of any domain, and hospitality and tourism industry is one of the front runners in implementing these technologies. The IoT has already started its impact all around the industry and tourism industry is thriving to match the pace to be smartly connected with its guests all the time in hotels transportation or attraction. The technology is still in the early stages of growth and implementation. The paper is divided into three sections of introduction, discussion on influence of Internet of Things technology in different fragments of tourism industry and the impacts of IoT concluded with a proposed model. The model presented in this paper analyzes the impacts of FIR technologies in particular of IOT in tourism domain.

The importance of care partner input in Alzheimer’s disease (AD) drug development

AbstractBackgroundIn AD, the role of the care partner (CP) is essential to therapeutic development due to their gradually increasing role over the course of disease progression. Many current trials require CPs to report on patient symptoms later in the disease. Studies have also suggested that the role of the care partner‐patient dyad may be linked to reliable outcomes in drug development. However, there are issues in data collection via patient proxy due to concerns around reliability of information and reporting bias.MethodsThe AD PACE WMM Study assessed needs and priorities of patients and CPs throughout AD through both qualitative and quantitative methods. In both phases of the WMM study, patients in the earlier stages of disease served as their own reporters: those with clinical pathology of AD (Group 1), with MCI (Group 2), and with mild AD (Group 3) served as their own reporters. CPs for those with moderate (Group 4) or severe AD (Group 5) reported what was important to them as CPs. Qualitative results were used to develop patient and CP surveys in which respondents rated the importance of 42 items.ResultsCPs (n=119) were mostly women in their mid‐50s with high SES statuses. The average age of the corresponding care recipients was approximately 80 years and most were women. In general, CPs rated items related to the emotional well‐being of the care recipient (e.g., having a sense of self‐worth; not feeling down, depressed, anxious, anxious, worried, stressed; not feeling like a burden to others) highly. When taken as a whole, mean CP ratings for what is important to them were equal to or lower than mean patient ratings, especially for items with lowest importance ratings (driving, not having difficulty with work, planning and organizing activities).ConclusionUnderstanding and identifying the multiple ways in which CPs can provide critical input in AD therapeutic development needs further investigation. The WMM study identified emotional well‐being as a key priority for CPs for those with moderate or severe AD. Future studies and guidance should examine opportunities for CP input on drug development as CPs provide critical perspectives on the AD patient experience.

Challenges and key lessons from the design and implementation of an international haemophilia registry supported by a pharmaceutical company.

IntroductionReal‐world data are lacking regarding the relationship between prospectively collected patient‐reported outcomes (PROs), clinical outcomes and treatment in people with haemophilia (PWH). The Expanding Communications on Hemophilia A Outcomes (ECHO) registry was designed to address this data gap, but a range of difficulties led to early study closure.AimTo describe the challenges faced and lessons learned from implementing a multinational haemophilia registry.MethodsThe Expanding Communications on Hemophilia A Outcomes was planned as a five‐year observational cohort study to collect data from 2000 patients in nine countries. Based on direct observations, feedback from patients enrolled in ECHO, challenges of the study design and input from study‐sponsor representatives, the ECHO Steering Committee systematically identified the challenges faced and developed recommendations for overcoming or avoiding them in future studies.ResultsThe study closed after two years because few countries were activated and patient recruitment was low. This was related to multiple challenges including delayed implementation, stringent pharmacovigilance requirements, objections of investigators and patients to the burden of multiple PROs, data collection issues, lack of resources at study sites, little engagement of patients and competing clinical trials, which further limited recruitment. At study closure, 269 patients had been enrolled in four of nine participating countries.ConclusionsResearchers planning studies similar to ECHO may want to consider the barriers identified in this global registry of PWH and suggestions to mitigate these limitations, such as greater patient involvement in design and analysis, clearer assessment and understanding of local infrastructure and potential changes to the administration of the study.

From Development To Global Implementation Of Special Olympics Fitness Programs For People With Intellectual Disabilities

PURPOSE: People with intellectual disabilities (ID) are at greater risk of obesity, diabetes, and cardiovascular disease compared to typically developing peers. People with ID face disparity and lack access to many healthcare services so community-based fitness and physical activity interventions can be crucial ways to improve health in this population at high risk. Yet, most fitness interventions are not designed for people with intellectual disabilities (ID), often due to cost, accessibility, and literacy level. Special Olympics Inc. (SOI), a leading non-profit sports organization for people with ID, has made it a priority to improve health in the ID population through increasing fitness and physical activity. METHODS: This case study describes the process of developing and implementing fitness models for people with ID. Special Olympics Inc. (SOI) assessed fitness activities being done in local Special Olympics Programs (SO Programs) for effectiveness, feasibility, replicability, and scalability. Then, SO Programs were funded to continue fitness activities and collect data. SOI endorsed three of these ‘fitness models’ that showed most promise. SOI then funded other SO Programs to implement the models between 2016-2018. The results from that implementation is assessed. RESULTS: 5481 individuals from 75 SO Programs in 48 countries participated. Key components of fitness models were group fitness sessions, including participants without ID, goal setting, and incentives. Over 90% of SO Programs collected baseline systolic and diastolic blood pressure in >80% of participants. Programs were less likely to have >80% completed baseline data for weight (76.7% of programs), height (75.0%), and BMI (75.0%). For those that started in 2016, >99% had data from two time points. In 2017, 88.8% had two data time points or more and in 2018 70.1% had data from 2 or more time points. SO Programs reported participants were empowered and were motivated by the incentives. Getting buy in from participants families and the community greatly improved the implementation. However, data collection issues were common. CONCLUSION: Based on the ability to enroll participants, collect data, and implement the activities, SOI fitness models may be a feasible fitness intervention for people with ID. Supported by CDC Grant U27 DD001156.

Optimizing Study-life Balance within Higher Education: A Comprehensive Literature Review

Purpose: The rise of the phrase Work Life Balance was bought up in 1986 when amid many Americans there was prevalence of detrimental work place practices like neglecting families, leisure activities and friends in order to achieve their study place goals. The significance of work-life balance has been gaining ground in recent years to grasp a wider range of groups, including students. Searching and finding a balance can be complex and challenging for many individuals and students. Research design, data and methodology: Through this paper we will explore how students balance the competing demands of work, study, and social activities. Several factors have increased imbalances within Educational organizations, and technology specifically has been influential. However, technology also provides a novel solution to this organizational performance management issue. A Study-Life Optimization model (SLO) is suggested, which incorporates information systems, analytics, and decision support into a Smart Service System. A general framework for this model, detailing data collection, measurement, and ethical issues is explained briefly. Results: Outcomes include improved WLB, greater perceived quality of life, and increased Educational organizational performance. Conclusions: This paper contributes to the relevant literature as it pays attention to the various students' of varying lifestyles school-work-personal lives. Findings of this study will provide a meaningful of the Work/school-life balance issues faced by students. The research could be helpful to the various stakeholders of a University, the curriculum designers, program coordinators etc.

Making the Case for Unrestricted and Indiscriminate Data Collection: (a Prerequisite for the Success of Digital Economy)

Proposition: Data will not be protected, at all. In other words, there will be unrestricted and indiscriminate data collection. And, privacy will undergo fundamental change. I do acknowledge and, in fact, admit that the preceding assertions are very radical given the stance of European Union and the propounders of AI ethics in different countries of the world on the issues of data collection, data protection and privacy. Even the business corridors are no strangers to the issues. Perhaps that is why the issue of trustworthiness is being promulgated in relation to data. Regardless, I still maintain my stance, grounding it in the matrix of reality, reasoning, law, and economic structuring. Also, the companies that are in control of technology landscape are either ‘ignoring’ ethics or resorting to them to avoid concrete government regulations. After all, a peculiar thing about technological advancement is that it precedes law making. Some experts have warned that the data regulations as it acts against AI oriented enterprises, whether old or new. Needless to say, regulation of AI will finally become redundant and, hence, counterintuitive as an idea when we achieve AGI (Artificial General Intelligence). In my opinion, academic and public discourse on data protection and data privacy are a deliberate attempt at misdirection. The work being done by ethics experts is not only counterintuitive to the direction in which the technological developments are unfolding but is also a desperate attempt in skewing the said development. The paper explains that in further detail. Data collection should be unrestricted, indiscriminate, and absolute. Why do I say that? There are many reasons but, primarily, the most important of them could be put forth as follows: 1) Data is a pre-requisite for digital economy. 2) Data is already being collected in the absence of any substantial laws or discarding the laws, in essence 3) More regulations do not conform to the principles of free market economy which the advanced economy stand with, in principle. 4) Regulations increase the cost in favour of a few and to the detriment of the rest of the players.

Avoiding GIGO: Learnings from data collection in innovation research

This paper follows a research project meant to focus on understanding how universities and public research organisations (PRO) collaborate with firms in order to innovate and develop products or services using the same technology, an invention called Controlled Radical Polymerisation (CRP). We say ‘meant to’ quite deliberately as this project was plagued with a range of data collection issues. We aim to give the reader a sense of the complications our research team faced as we share and highlight the issues we did not properly consider in the process of collecting our data, and the strategies we implemented to save the project and collect useful data. These issues included the benefits of the choice of a face-to-face methodology, using our own networks to recruit participants, the underestimated influence of secrecy when collaborating with industry on an innovation project, the value of having a “local champion” and having specific procedures in place for operational matters; but most of all the importance of being flexible and agile.We share the lessons we learned during this journey. Importantly, we seek to give some practical advice and a sense of the reality of data collection – a reality which is often smoothed over and written up as unproblematic in academic publications.

Process Control Charts in Falls Prevention: The Experience of the Local Healthcare Authority of Romagna, Italy.

Patient safety is a great concern of healthcare institutions and the correct reporting and management of adverse events is a key element for supporting safety improvement efforts. Patient falls are the most frequent adverse event in hospitals and often cause serious patient outcomes. In this work, we describe the experience of the Local Healthcare Authority of Romagna, Italy that, within the framework of a quality and safety improvement programme, designed, developed and implemented a desktop application for monitoring inpatient falls. A multidisciplinary team created a software tool based on R, an open source software for statistical computing that, appropriately combined with the existing hospital information system, is used to obtain Shewhart u-control charts for monitoring the monthly fall rates. The tool had been implemented in twenty nine hospital units. The results indicate that the proposed application gave a valuable contribution in the safety improvement activities. Its usefulness extended beyond the “safety problem” as it also enabled hospital managers to identify a number of critical issues in data collection. As a result, where necessary, improvement actions had been implemented. Furthermore, the use of open source software led to a considerable cost reduction and facilitated customization of the software tool.

Secure and Efficient Data Collection and Storage of IoT in Smart Ocean

Due to the abundant marine resources, smart ocean has attracted much attention of the government, industry, and academy. The Internet-of-Things (IoT) architectures for smart ocean have been proposed to collect various of data from the ocean, thereby assisting environmental protection, military reconnaissance, and so on. However, few researchers have paid attention to the security and privacy issues of data collection and transmission. In this article, for the unreliable underwater environment, we present a secure, efficient, and complete data collection, and transmission and storage scheme for IoT in smart ocean. Especially, to prolong the lifetime of the underwater node, two novel data compression algorithms [lossy data compression algorithm (LCA) and lossless data compression algorithm (NLCA)] are also proposed. Moreover, due to the vulnerability of underwater nodes, we also propose a corresponding IoT framework and data collection pattern to resist the single point failure attack. Besides, to guarantee the confidentiality, reliability, and integrity of transmitting data, Elliptic Curve-ElGamal (EC-ElGamal) and elliptic curve digital signature algorithm (ECDSA) are employed. The consensus algorithm and blacklisting mechanism are also employed to detect and address failure or malicious nodes. Finally, the security analysis demonstrates that our scheme is able to resist many typical attacks for underwater nodes, such as manipulation attacks, Distributed Denial-of-Service (DDoS) attacks, malicious node injection attacks, and so on. Additionally, relevant experimental results show that the scheme is feasibility and efficiency.